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With the rapid development of society, the demand for information storage is increasing. Hence, research involving magnetic storage materials is gaining importance. In this study, Fe3Mn3Co60.66Si33.34 powders were prepared via mechanical alloying and sintering. The effect of the sintering temperature on the crystal structure was analyzed by X-ray diffraction, and the results showed that doped Co2Si powder was successfully prepared. Vibrating sample magnetometry was used to investigate the magnetic properties of Fe3Mn3Co60.66Si33.34, and showed that the coercivity of the samples decreased with increasing sintering temperature. Fe3Mn3Co60.66Si33.34 sintered at 950 °C yielded a magnetic saturation strength of 36.42 emu/g, coercivity of 135.92 Oe, and remanent magnetization of 4.97 emu/g. Electron microscopy analyses showed that the prepared particles were spherical, and the average grain size increased with increasing sintering temperature. The electromagnetic loss was analyzed by simulating electromagnetic parameters, which revealed that the electromagnetic loss tangent decreased with increasing sintering temperature. Hence, it is inferred that Fe3Mn3Co60.66Si33.34powder materials sintered at high temperatures are expected to have suitable properties for magnetic storage applications.

Artificial intelligence (AI) has enabled a new paradigm of smart applications -- changing our way of living entirely. Many of these AI-enabled applications have very stringent latency requirements, especially for applications on mobile devices (e.g., smartphones, wearable devices, and vehicles). Hence, smaller and quantized deep neural network (DNN) models are developed for mobile devices, which provide faster and more energy-efficient computation for mobile AI applications. However, how AI models consume energy in a mobile device is still unexplored. Predicting the energy consumption of these models, along with their different applications, such as vision and non-vision, requires a thorough investigation of their behavior using various processing sources. In this paper, we introduce a comprehensive study of mobile AI applications considering different DNN models and processing sources, focusing on computational resource utilization, delay, and energy consumption. We measure the latency, energy consumption, and memory usage of all the models using four processing sources through extensive experiments. We explain the challenges in such investigations and how we propose to overcome them. Our study highlights important insights, such as how mobile AI behaves in different applications (vision and non-vision) using CPU, GPU, and NNAPI. Finally, we propose a novel Gaussian process regression-based general predictive energy model based on DNN structures, computation resources, and processors, which can predict the energy for each complete application cycle irrespective of device configuration and application. This study provides crucial facts and an energy prediction mechanism to the AI research community to help bring energy efficiency to mobile AI applications., Accepted in IEEE International Conference on Communications (ICC); 28 May - 01 June 2023; Rome, Italy; six pages, seven figures, two tables

3GPP has introduced Private 5G to support the next-generation industrial automation system (IAS) due to the versatility and flexibility of 5G architecture. Besides the 3.5GHz CBRS band, unlicensed spectrum bands, like 5GHz, are considered as an additional medium because of their free and abundant nature. However, while utilizing the unlicensed band, industrial equipment must coexist with incumbents, e.g., Wi-Fi, which could introduce new security threats and resuscitate old ones. In this paper, we propose a novel attack strategy conducted by a mobility-enabled malicious Wi-Fi access point (mmAP), namely \textit{PACMAN} attack, to exploit vulnerabilities introduced by heterogeneous coexistence. A mmAP is capable of moving around the physical surface to identify mission-critical devices, hopping through the frequency domain to detect the victim's operating channel, and launching traditional MAC layer-based attacks. The multi-dimensional mobility of the attacker makes it impervious to state-of-the-art detection techniques that assume static adversaries. In addition, we propose a novel Markov Decision Process (MDP) based framework to intelligently design an attacker's multi-dimensional mobility in space and frequency. Mathematical analysis and extensive simulation results exhibit the adverse effect of the proposed mobility-powered attack., 6 pages, 7 Figures, Accepted in IEEE International Conference on Communications 2023

Syndromic deafness caused by PTPN11 gene mutation has gradually come into the public’s view. In the past, many people did not understand its application mechanism and role and only focused on non-syndromic deafness, so the research on syndromic deafness is not in-depth and there is a large degree of lack of research in this area. In order to let the public know more about the diagnosis and gene function of deafness caused by PTPN11 gene mutation syndrome, this paper used deep learning technology to study the diagnosis and gene function of deafness caused by syndrome with the concept of intelligent medical treatment, and finally drew a feasible conclusion. This paper provided a theoretical and practical basis for the diagnosis of deafness caused by PTPN11 gene mutation syndrome and the study of gene function. This paper made a retrospective analysis of the clinical data of 85 deaf children who visited Hunan Children’s Hospital,P.R. China from January 2020 to December 2021. The conclusion were as follows: Children aged 1–6 years old had multiple syndrome deafness, while children under 1 year old and children aged 6–12 years old had relatively low probability of complex deafness; girls were not easy to have comprehensive deafness, but there was no specific basis to prove that the occurrence of comprehensive deafness was necessarily related to gender; the hearing loss of patients with Noonan Syndrome was mainly characterized by moderate and severe damage and abnormal inner ear and auditory nerve; most of the mutation genes in children were located in Exon1 and Exon3, with a total probability of 57.65%. In the course of the experiment, it was found that deep learning was effective in the diagnosis of deafness with PTPN11 gene mutation syndrome. This technology could be applied to medical diagnosis to facilitate the diagnosis and treatment of more patients with deafness with syndrome. Intelligent medical treatment was also becoming a hot topic nowadays. By using this concept to analyze and study the pathological characteristics of deafness caused by PTPN11 gene mutation syndrome, it not only promoted patients to find diseases in time, but also helped doctors to diagnose and treat such diseases, which was of great significance to patients and doctors. The study of PTPN11 gene mutation syndrome deafness was also of great significance in genetics. The analysis of its genes not only enriched the gene pool, but also provided reference for future research.

Two unprecedented aconitine-type C19-diterpenoid alkaloids, refractines A–B (1–2), along with 12 known compounds (3–14) were isolated from the roots of Aconitum refractum (Finet et Gagnep.) Hand. -Mazz. Their structures were elucidated based on extensive spectroscopic analyses, including 1D and 2D NMR, IR, and HR-ESI-MS data. All compounds were tested for inhibitory activities against NO production in LPS induced RAW 264.7 macrophages, compounds 10 and 14 had slight inhibited NO production with rate of 29.4% and 22.1% at the concentration of 30 μM, respectively.

Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST.

During the postconstruction operation period, the engineering structure and operation safety of a pipe-jacking tunnel are more likely to be threatened by the surrounding environment and groundwater when it is located in a soft clay stratum. Surface settlement data related to the construction of 24 rectangular pipe-jacking tunnels in a soft clay stratum in China were collected and analyzed to determine the effect of pipe-jacking tunnel engineering on its deformation based on factors such as structural section form, geotechnical condition, groundwater condition, and buried depth. The postmaximum surface settlement and postformation loss rate were obtained, the correlation coefficients between the postsurface deformation of soft clay strata and various influencing factors were calculated using a software for correlation analysis, and the variation rules for each influencing factor were explored in depth. Subsequently, multiple linear regression methods were used to predict the maximum surface deformation of the pipe-jacking tunnel project in soft clay strata during the postoperation period, and actual engineering cases were verified and analyzed. The results show that the relative buried depth of the pipe-jacking tunnel in soft clay stratum has the greatest influence on the postmaximum ground settlement, followed by the section area of the pipe-jacking tunnel, and the relative height coefficient of groundwater level has the least influence. Under the reduction coefficient of 0.937 − 0.846, a new prediction formula for surface settlement deformation after the construction of the soft clay layer was derived. The error between the formula and the measured data was 9.05%, which can be used as a reference for controlling design parameters to predict the settlement after construction of pipe-jacking engineering.

A Mobility Digital Twin is an emerging implementation of digital twin technology in the transportation domain, which creates digital replicas for various physical mobility entities, such as vehicles, drivers, and pedestrians. Although a few work have investigated the applications of mobility digital twin recently, the extent to which it can facilitate safer autonomous vehicles remains insufficiently explored. In this paper, we first propose visualization of mobility digital twin, which aims to augment the existing perception systems in connected and autonomous vehicles through twinning high-fidelity and manipulable geometry representations for causal traffic participants, such as surrounding pedestrians and vehicles, in the digital space. An end-to-end system framework, including image data crowdsourcing, preprocessing, offloading, and edge-assisted 3D geometry reconstruction, is designed to enable real-world development of the proposed visualization of mobility digital twin. We implement the proposed system framework and conduct a case study to assess the twinning fidelity and physical-to-digital synchronicity within different image sampling scenarios and wireless network conditions. Based on the case study, future challenges of the proposed visualization of mobility digital twin are discussed toward the end of the paper., Comment: The paper has been accepted by The 20th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2023)

In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable problems in noisy environments, and iterative calculations for clusters with a large number of different sample sizes are not accurate and efficient. In this paper, starting from the strategy of large-scale priority, the data is fuzzy iterated using granular-balls, and the membership degree of data only considers the two granular-balls where it is located, thus improving the efficiency of iteration. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios, which enhances the practicability of fuzzy clustering calculations.

Rational nitrogen (N) application can greatly increase rice (Oryza sativa L.) yield. However, excessive N input can lead not only to low N use efficiency (NUE) but also to severe environmental pollution. Reducing N application rate with a higher planting density (RNHD) is recommended to maintain rice yield and improve NUE. The effects of RNHD on fertilizer N fate and rice root growth traits remain unclear. We accordingly conducted a two-year field experiment to investigate the influence of RNHD on rice yield, fertilizer 15N fate, and root growth in a double-rice cropping system in China. In comparison with the conventional practice of high N application with sparse planting, RNHD resulted in similar yield and biomass production as well as plant N uptake. RNHD increased agronomic NUEs by 23.3%–31.9% (P

Currently, an increasing number of long noncoding RNAs (LncRNAs) have been reported to be abnormally expressed in human carcinomas and play a vital role in tumourigenesis. Some studies were carried out to investigate the influence of the expression of plasmacytoma variant translocation 1 (PVT1) on prognosis and its clinical significance in patients with breast cancer, while the results were contradictory and uncertain. A meta-analysis was conducted with controversial data to accurately assess the issue.

Existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Inspired by adaptive process of granular-ball division and differentiation, we present a novel clustering approach that retains the speed and efficiency of K-means clustering while out-performing time-tested density clustering approaches widely used in industry today. Our simple, robust, adaptive granular-ball clustering method can efficiently recognize clusters with unknown and complex shapes without the use of extra parameters. Moreover, the proposed method provides an efficient, adaptive way to depict the world, and will promote the research and development of adaptive and efficient AI technologies, especially density computing models, and improve the efficiency of many existing clustering methods., 5 pages, 1 figures

Tissue dielectric properties can vary upon the incident of an acoustic wave. The goal of this study is to quantify this change due to the acoustoelectric effect (AE), and to obtain the frequency-dependent dielectric properties of tissues exposed to low-intensity focused ultrasound (LIFU). The dielectric properties of the blood, brain, chest muscle, heart, kidney, leg muscle, liver, lung, pancreas, and spleen of rats were measured by an open-ended coaxial probe method. The acoustic intensity of LIFU focus was 2.97 MPa (67.6 W/cm2), 3.95 MPa (120 W/cm2), and 5.17 MPa (204 W/cm2), respectively, and the measurement frequency band was 0.1–7.08 GHz. The measurement results show that with the LIFU modulation, the conductivity and dielectric constant decreased in the high-frequency band, and on the contrary, they increased in the low-frequency band, and the larger the acoustic intensity was, the more obvious the phenomenon was. This work contributes to a better understanding of the mechanisms by which ultrasound acts on the dielectric properties of biological tissues. It is expected that the findings from this study will provide a basis that the response of tissue to LIFU modulation can be monitored by noninvasive techniques such as microwave-induced thermoacoustic imaging (MTI) and microwave imaging, present a new idea for improving the endogenous contrast between different biological tissues in MTI and acoustoelectric imaging, and possibly lead to the development of a new imaging method based on the relaxation time of tissue after LIFU modulation.

Additional file 1. Detection results of clinical samples using LAMP-Microfluidic Chip.

Additional file 4. PCR primers of target genes.

It is crucial to evaluate the quality and determine the optimal number of clusters in cluster analysis. In this paper, the multi-granularity characterization of the data set is carried out to obtain the hyper-balls. The cluster internal evaluation index based on hyper-balls(HCVI) is defined. Moreover, a general method for determining the optimal number of clusters based on HCVI is proposed. The proposed methods can evaluate the clustering results produced by the several classic methods and determine the optimal cluster number for data sets containing noises and clusters with arbitrary shapes. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.

To research the failure of carbon fiber-reinforced composite laminated specimens, the tensile tests and compressive tests were conducted for [90]16 and [0]16 specimens, and the shear tests were conducted for [±45]4s specimens, and the microscopic failure mechanisms were observed by scanning electron microscopy. To research the failure and energy absorption of different thin-walled structures with different layups, the quasi-static axial crushing tests were conducted for [±45/0/0/90/0]s and [0/90]3s circular tubes, [0/90]3s and [±45]3s square tubes, [0/90]4s and [±45]4s sinusoidal specimens, and the internal failure were further investigated by 3D X-ray scan. Based on the load-displacement curves, the energy absorptions were evaluated and compared according to specific energy absorption and peak crushing force, and the relationships between failure modes and specific energy absorption, peak crushing force were further researched. The results show that the macroscopic failure modes are the collective results of varieties of microscopic failure mechanisms, such as fiber fracture, matrix deformation and cracking, interlamination and intralamination cracks, cracks propagation, etc. The [±45/0/0/90/0]s circular tube shows the transverse shearing failure mode with high specific energy absorption. The [±45]3s square tube and [±45]3s sinusoidal specimen show the local buckling failure mode with low specific energy absorption. The [0/90]4s sinusoidal specimen, [0/90]3s circular tube, and [0/90]3s square tube show the lamina bending failure mode with medium specific energy absorption. The failure mode of thin-walled structure can be changed by reasonable layups design, and the energy absorption can further be improved.

Based on electromechanical impedance (EMI) technology, we studied the indication effect of each characterization index on flange bolt looseness under different noise levels. A new evaluation index T is constructed to solve the problem that the minor looseness and noise under the actual monitoring environment cannot be distinguished with existing indexes. The results show that among the five characterization indexes, the root mean square deviation (RMSD) and the correlation coefficient deviation (CCD) can reflect the minor looseness sensitively and are positively correlated with the degree of looseness. They also have good performance of looseness identification under the noise of 30 dBW. However, the indexes' indication range of the case under 1 dBW-6 dBW noise overlaps with the case under 36 N·m-39.5 N·m torque. So it is impossible to judge the structure state with mere characterization indexes. Evaluation index T can extract the correlation difference of the conductance spectrum under the noise conditions and looseness conditions with the benchmark respectively. And there are different indication ranges of T on the noise and minor looseness. By determining a threshold and comparing T with it, whether bolt looseness occurs in an unknown condition can be effectively judged. Finally, T is applied to other flange structure with 8 bolts to verify the effectiveness and general applicability of the method. The evaluation index plays an important role in avoiding misjudgment of flange bolt caused by noise influence on minor looseness and improving the ability of identification when the bolt is at the critical state.

Purpose This is a clinical study to compare complete digital workflows generated using intraoral scanning and the split-file technique with a conventional workflow for cement-retained implant-supported restorations. Materials and methods Forty patients requiring posterior single-unit implant restorations were included. Twenty patients were randomly assigned to the complete digital workflow group, involving intraoral scanning and manufacture of cement-retained crowns using the split-file technique (test group). The remaining 20 patients were assigned to the hybrid workflow group (control group), involving conventional impressions and CAD/CAM-fabricated crowns based on stone casts. Scanning of the crowns was performed before and after clinical adjustment using an intraoral scanner (TRIOS Color; 3Shape). Two 3D digital models were trimmed and superimposed to evaluate changes in dimensions using Geomagic Control 2014 software. Chair-side and laboratory times for the entire workflow were recorded. Independent-sample t tests were used for the statistical analysis. Results All crowns were inserted without re-fabrication. The average maximum occlusal adjustment of the crowns, measured as maximum deviation of occlusal area in superimposed pre and post scans, was -212.7 ±150.5 and -330.7 ±192.5 μm in the test and control groups, respectively (p = 0.037).The average area of occlusal adjustment, measured as area of deviation larger than 100 μm, was 8.4 ±8.1 and 17.1 ±12.3 mm2 in the test and control groups, respectively (p = 0.012). The mesial and distal contact adjustment amounts, maximum deviations of proximal area, were -33.0 ±96.2 and -48.6 ±70.5 μm in the test group, and -3.7 ±66.7 μm and -11.4 ±106.7 μm in the control group, respectively. The mean chair-side time was 20.20 ± 3.00 and 26.65 ± 4.53 min in the test and control groups, respectively (p Conclusions Single-unit cement-retained crowns with complete digital workflows required fewer crown adjustments and had shorter clinical and laboratory times compared to conventional impressions and hybrid workflows. Digital impressions and the split-file technique provided customized abutments and cement-retained crowns, thus broadening the indications for digital workflows for implants. This article is protected by copyright. All rights reserved.

The long-term use of a piezoelectric smart structure make it difficult to judge whether the structure or piezoelectric lead zirconate titanate (PZT) is damaged when the signal changes. If the sensor fault occurs, the cases and degrees of the fault are unknown based on the electromechanical impedance method. Therefore, after the principal component analysis (PCA) of six characteristic indexes, a two-component solution which could explain 99.2% of the variance in the original indexes was obtained to judge whether the damage comes from the PZT. Then LibSVM was used to make an effective identification of four sensor faults (pseudo soldering, debonding, wear, and breakage) and their three damage degrees. The result shows that the identification accuracy of damaged PZT reached 97.5%. The absolute scores of PCA comprehensive evaluation for structural damages are less than 0.5 while for sensor faults are greater than 0.6. By comparing the scores of the samples under unknown conditions with the set threshold, whether the sensor faults occur is effectively judged; the intact and 12 possible damage states of PZT can be all classified correctly with the model trained by LibSVM. It is feasible to use LibSVM to classify the cases and degrees of sensor faults.

To analyze the failure mechanism and energy-absorbing characteristics of composite thin-walled C-channels subject to the low-speed axial compression, 12 groups of T700/MTM28 specimens with three di...

Piezoelectric sensor is a crucial part of electromechanical impedance technology whose state will directly affect the effectiveness and accuracy of structural health monitoring (SHM). So carrying out sensor self-diagnosis is important and necessary. However, it is still difficult to distinguish sensor faults from structural damage as well as identify the cases and degrees of sensor faults. In the study, three characteristic indexes of admittance which have different indication intervals for damages of structure and sensors were selected from six indexes after comparison. To improve the discrimination effect, three principal components (PC) were extracted by principal component analysis (PCA). And the damage information represented by PCs was clustered by the K-means algorithm to identify the cases of damage. Then, the degrees of sensor damages were classified with the artificial neural network (ANN). The results show that the K-means clustering analysis based on admittance characteristics can accurately distinguish and identify the structural damage and four kinds of sensor damages, namely, pseudosoldering, debonding, wear, and breakage. The trained ANN model has a good recognition effect on the damage degrees and the accuracy of recognition reaches 100%. This study has a certain reference value for piezoelectric sensor self-fault identification.

Background: Fluoroquinolones are one of the most widely used antibiotics in the treatment of respiratory tract infections, and the mechanism of resistance to fluoroquinolones is considered to be related to the amino acid substitution of gyrA, gyrB, parC, and parE, the Quinolone Resistance-Determining Regions (QRDRs) of DNA cyclase type II topoisomerase and IV topoisomerase. The purpose of this study was to explore the molecular mechanism of quinolone resistance of Nontypeable Haemophilus influenzae(NTHi ) isolates and analyze the mutation law of the QRDRs gene. Results: MIC value of ofloxacin of 280 NTHi isolates from lower respiratory tract secretions in children group during 2003~2004 and in whole age group during 2013~2014 in Western Sichuan, China were monitored and the amino acid sequences of gyrA, gyrB, parC and parE gene in QRDRs were detected. The resistance rate of ofloxacin in adult group was 1.92% (n=52), while the NTHi strains with ofloxacin MIC value≥0.5 showed an upward trend in all age groups. No ofloxacin-resistant strains were found in 57 NTHi strains isolated from the children patient. Four amino acid substitutions were found in GyrA genes, four in GyrB, three in parC and nine in parE genes. The results showed that different amino acid replacement patterns of the gyrA , gyrB, parC and parE gene had different effects on ofloxacin MIC values. Conclusions: Ser-84-leu and asp-88-tyr/asn mutation of the gyrA, ser-84-lys/ile and ser -133-ala mutations of the parC and ala-400-val variation of the gyrB were the main factors leading to the increase of MIC value of ofloxacin of NTHi strains in Western Sichuan, China. It can be predicted that with the increase of quinolones exposure, the susceptibility of isolates from children to quinolones will be further reduced.

Periodontitis is the sixth-most prevalent chronic inflammatory disease and gradually devastates tooth-supporting tissue. The complexity of periodontal tissue and the local inflammatory microenvironment poses great challenges to tissue repair. Recently, stem cells have been considered a promising strategy to treat tissue damage and inflammation because of their remarkable properties, including stemness, proliferation, migration, multilineage differentiation, and immunomodulation. Several varieties of stem cells can potentially be applied to periodontal regeneration, including dental mesenchymal stem cells (DMSCs), nonodontogenic stem cells, and induced pluripotent stem cells (iPSCs). In particular, these stem cells possess extensive immunoregulatory capacities. In periodontitis, these cells can exert anti-inflammatory effects and regenerate the periodontium. Stem cells derived from infected tissue possess typical stem cell characteristics with lower immunogenicity and immunosuppression. Several studies have demonstrated that these cells can also regenerate the periodontium. Furthermore, the interaction of stem cells with the surrounding infected microenvironment is critical to periodontal tissue repair. Though the immunomodulatory capabilities of stem cells are not entirely clarified, they show promise for therapeutic application in periodontitis. Here, we summarize the potential of stem cells for periodontium regeneration in periodontitis and focus on their characteristics and immunomodulatory properties as well as challenges and perspectives.

Additional file 1: Supplementary Table Reference range of laboratory values and STONP model. Supplementary Figure Barcode links to STONP online tool and App.

Additional file 2: Table 1. Results of qSOFA score statistical analysis. Figure 1 Mann Whitney U Test of qSOFA in the Discharged and Death Groups. Figure 2 ROC Curve of qSOFA Score.

Chlorophenols with their toxicity, persistence and detrimental effects on the environment raise great concerns. Biological treatment seems to be the most economical way when dealing with wastewater containing these chlorinated compounds. Anaerobic dechlorination of chlorophenols has been extensively investigated. Despite it being effective in degrading chlorophenols, the process suffers from instabilities during operation. The imbalanced growth kinetics between acidogens and methanogens, accumulation of fermentative products leading to acidification of the system and methanogens’ sensitivity to toxic compounds, like chlorophenols, make the whole process less stable and difficult to maintain. To solve the problem of process instability, acidogenic dechlorination of chlorophenols as pretreatment becomes a possible alternative. However, there are concerns still unaddressed such as: 1) To date, very few findings have been reported on acidogenic dechlorination process. Contradictory conclusions were drawn among different researchers; 2) Acidic condition is not the appropriate environment for the growth of chlororespirating bacteria; 3) The role of methanogens in dechlorination is arguable as different researchers reported contradictory findings. Excluding methanogens in the acidogenic environment for dechlorination is not conclusive in this case. In this project, the anaerobic sequencing batch reactor (anSBR) was acclimated and operated at pH 5.5. The reduction of pH followed the step-wise reduction manner at the rate of 0.5 per week. The acclimated acidic sludge demonstrated capability to dechlorinate 2-MCP, 2,4,6-TCP and PCP but this was limited to attacking ortho-chlorines only. Besides the long acclimation period (80 days) initially, the acclimated sludge was dechlorinated TCP and PCP without lag phase. When inhibited, caused by PCP overloading and extreme low pH (2.0), dechlorination was severely curtailed. It was found the inhibition was associated with methanogen’s activity curtailment, whereas acidogenesis still continued. The qPCR analysis of microbial communities revealed the Methanosarcinaceae family was the only group, which dropped sharply in terms of copy number. These results led to the hypothesis that methanogens participated in the dechlorination of chlorophenols under acidic condition. Specific inhibitors to methanogens and gram-positive bacteria were applied to investigate the roles of different groups of anaerobes in dechlorination. Both inhibitors affected dechlorination, with the methanogenic specific inhibitor (BES) inhibiting dechlorination of 2-MCP no matter which electrons were applied. The antibiotic of gram-positive bacteria (vancomycin) did inhibit dechlorination as well. In addition, more methane was produced in batches with dosage of vancomycin suggested that acetate was directed to methanogenesis from other pathways during the inhibition. The findings in this study implied a syntrophism was involved in dechlorination. Both methanogens and the acetate-oxidizing bacteria should be properly maintained in order to perform dechlorination of 2-MCP under acidic condition. The results found in this project implied it was not possible to exclude methanogens from the dechlorination process hence the instability issues associated with the conventional anaerobic dechlorination may not be solved by phase separation in the traditional sense. Community analysis of the sludge did suggest there were in fact limited phenotypes of methanogens, which could survive and participate in the dechlorination process. The system preferentially selected certain groups of methanogens, which demonstrated the ability to survive in the low pH environment. This project confirmed the notion the acidic reactor could dechlorinate chlorophenols but with the participation of methanogens and acetate-oxidizing bacteria. The process was therefore not strictly phase separated but was shown to be a viable treatment process for chlorophenols. Doctor of Philosophy (CEE)

The accuracy of structural state evaluation may be affected by the damaged piezoelectric sensors. Therefore, it is necessary to identify the sensor fault during monitoring. This paper proposes a method based on classification and regression tree (CART) and particle swarm optimization (PSO) to improve the efficiency of potential feature sets selection for sensor fault classification and build an identification model with the best performance. Firstly, the signal features of three structural changes and four sensor faults were extracted with five indexes. Then the decision trees (DT) for sensor fault classification were built based on different index combinations whose performances were then evaluated by the designed fitness function. Finally, PSO was used to optimize the searching for the best index combination. The results show that compared with the exhaustive method, adopting PSO for DT optimization can greatly simplify the search process. When the particle population is 5 and 10, the fitness converges to the optimal solution after only 6 and 4 iterations respectively. Although the DT with the best fitness is trained with only two indexes, its accuracy is higher than those trained with more indexes and the classification accuracy of 64 samples reaches 98.4% which shows the feasibility and practicability of the method.

In the electromechanical impedance-based health monitoring of structures, partial failure of piezoelectric lead zirconate titanate will result in signal changes, which may cause misjudgment of the structure state. Therefore, this paper proposes an evaluation method of the sensor condition based on k-nearest neighbors (kNNs) and least-squares regression (LSR) to make monitoring more reliable. After the analysis of the signal characteristics of three structural changes and four sensor faults, the principal components (PCs) of three indices are extracted by principal component analysis. Next, the kNN classifier is trained with the data represented by PCs and then tested by tenfold cross-validation. To determine the degree of sensor faults, LSR is used to fit the damage degree laws with multivariate nonlinear equations. The results show that the kNN model trained with three PCs has higher classification accuracy than the one trained with two PCs. The accuracy of the former reaches 100%. The R2 values of damage degree regressions of four sensor faults are all greater than 0.85, and the p-values are far less than 0.05, which denotes the effectiveness of the regression in the prediction of damage degree. This method has great application potential in evaluating sensor conditions accurately and quickly.

The enhanced dewaterability of waste activated sludge (WAS) by thermal, Fe (II), or thermal-Fe (II) activated oxone treatments was systematically investigated and compared. It was noted oxone oxidation induced by Fe (II) at the lowest tested temperature (25 °C) was more effective than any tested temperature alone induced oxone oxidation (25–80 °C). Temperature at mesophilic range can enhance the efficiency of Fe (II)-oxone treatment by 20%. Further increase of temperature to higher than 35 °C, e.g. 50 °C, did not remarkably improve the filterability of sludge in Fe (II)–oxone experiment. On the other hand, the worst performance was from thermophilic condition (80 °C) in Fe (II)-oxone experiment. The results demonstrated a significant combined effect of Fe (II)-oxone on improving sludge dewaterability within mild temperature ranging from 25 to 35 °C. Higher sludge filterability and flowability, as well as lower sludge viscosity were observed after treatment. The improvement of sludge dewaterability was achieved by removal of organic compounds in loosely-bound (LB) and tightly-bound (TB) extracellular polymeric substances (EPS). Furthermore, the improved sludge filterability was strongly correlated with the removal of dissolved organic matter (DOC), tyrosine-like proteins and fulvic acid-like materials in LB EPS. Economic analysis suggested the mesophilic Fe (II)-oxone treatment was promising for improving WAS dewaterability compared with the conventional thermal treatment.

This study aimed to develop an in-situ sludge pretreatment method by increasing the temperature from thermophilic to extreme thermophilic condition in a single-stage anaerobic digester. The results revealed that a stable performance was obtained within the temperature range of 55–65 °C, and the maximum methane yield of 208.51 ± 13.66 mL/g VS was obtained at 65 °C. Moreover, the maximum extent of hydrolysis (33%) and acidification (27.1%) was also observed at 65 °C. However, further increase of temperature to 70 °C did not improve the organic conversion efficiency. Microbial community analysis revealed that Coprothermobacter , highly related to acetate oxidisers, appeared to be the abundant bacterial group at higher temperature. A progressive shift in methanogenic members from Methanosarcina to Methanothermobacter was observed upon increasing the temperature. This work demonstrated single-stage sludge digestion system can be successfully established at high temperature (65 °C) with stable performance, which can eliminate the need of conventional thermophilic pretreatment step.

The mechanism of Fe (II) – oxone conditioning to improve sludge dewaterability was investigated in this study. Five different types of sludge were tested, including raw sludge (Group 1: mixed primary and secondary sludge, waste activated sludge and anaerobic digested sludge) and pretreated sludge with prior solubilisation (Group 2: ultrasonic or thermal pretreated sludge). After Fe (II) – oxone conditioning, the concentrations of dissolved organic carbon, protein and polysaccharide of soluble extracellular polymeric substances (SB EPS) increased for Group 1, but decreased for Group 2. For all types of sludge investigated, the related organic compounds of loosely bound (LB) and tightly bound (TB) EPS decreased with Fe (II) – oxone conditioning, and increased sludge filterability showed strong and positive correlation with the removal of low molecular weight protein and neutrals in LB EPS. Fe (II) - oxone was very effective in disintegrating cell membrane and caused potential cell lysis, as indicated by increased percentage of damaged microbial cells. From this study, the mechanism of Fe (II) - oxone conditioning was proposed and can be divided into two steps: (1) Oxidation step – sulfate radicals degraded organic compounds in LB and TB EPS in sludge and transformed bound water to free water that was trapped in TB and LB EPS; It also damaged cells membrane and may help to release intracellular water content. Sludge flocs were broken into smaller particles; (2) Coagulation step - Fe (III), generated from the oxidation step can act as a coagulant to agglomerate smaller particles into larger ones and reduce the repulsive electrostatic interactions. Combined effects from above two steps can greatly improve sludge filterability. Accepted version

This study investigated the mechanism and effects of ultrasonic pretreatment followed by acidification on sludge dewaterability through looking at the changes of extracellular polymeric substances (EPS) content, composition and stratification. The results suggested sludge filterability was closely correlated with quantity of protein (R = 0.94, p < 0.01) and polysaccharide (R = 0.97, p < 0.01) present in loosely bound EPS rather than in soluble and tightly bound EPS. The fractions of polymers, especially tryptophan-like proteins and microbial by-product like material at molecular weight of 106–5 × 107 Da, were the key compounds related to sludge filterability. Ultrasonication may increase biopolymers concentrations that in turn deteriorate sludge filterability as evidenced at high ultrasonic power conditions. However, the subsequent acidification can reduce the concentrations of these organic compounds, reduce negative zeta potential, and increase floc size, thus increase sludge filterability. Combined ultrasonic-acid pretreamtent was more effective than the acidification treatment alone in reducing the concentrations of macromolecular compounds that may deteriorate sludge filterability.

With the development of social economy, the demands for electric power are increasing, and the direct load supply on 10kV side of 220kV transformer substation increasingly common. Under such mode, the difficulty in Automatic Voltage Control (AVC) voltage / reactive power control caused by voltage regulating conflict between the 10kV side and the 220kV and 110kV side is also becoming more prominent. In this article, the effect of AVC strategy and combined strategy on the quality of electric energy is discussed; the voltage regulating problems of direct load supply are classified and discussed; the solution for on-line computation of the sensitivity of tapping point voltage regulation, regulating the main transformer unloaded tap position and adjusting the AVC control parameters are put forward, whose application in the voltage and reactive power optimization and improvement of Yibin’s power grid is significantly effective.

A new species of the genus Amolops Cope, 1865 is described from Nyingchi, southeastern Tibet, China, based on morphological and molecular data. The new species, Amolops nyingchiensis sp. nov. is assigned to the Amolops monticola group based on its skin smooth, dorsolateral fold distinct, lateral side of head black, upper lip stripe white extending to the shoulder. Amolops nyingchiensis sp. nov. is distinguished from all other species of Amolops by the following combination of characters: (1) medium body size, SVL 48.5-58.3 mm in males, and 57.6-70.7 mm in females; (2) tympanum distinct, slightly larger than one third of the eye diameter; (3) a small tooth-like projection on anteromedial edge of mandible; (4) the absence of white spine on dorsal surface of body; (5) the presence of circummarginal groove on all fingers; (6) the presence of vomerine teeth; (7) background coloration of dorsal surface brown, lateral body gray with yellow; (8) the presence of transverse bands on the dorsal limbs; (9) the presence of nuptial pad on the first finger in males; (10) the absence of vocal sac in males. Taxonomic status of the populations that were previously identified to A. monticola from Tibet is also discussed.

Based on the transfer learning, we design a bird species identification model that uses the VGG-16 model (pretrained on ImageNet) for feature extraction, then a classifier consisting of two fully-connected hidden layers and a Softmax layer is attached. We compare the performance of the proposed model with the original VGG16 model. The results show that the former has higher train efficiency, but lower mean average precisions(MAP). To improve the MAP of the proposed model, we investigate the result fusion mode to form multi-channel identification model, the best MAP reaches 0.9998. The number of model parameters is 13110, which is only 0.0082% of the VGG16 model. Also, the size demand of sample is decreased., Comment: 11 pages,11 figures

The effects of temperature on nitrous oxide (N2O) accumulation during denitrification and denitritation were investigated. Batch experiments were performed to measure N2O accumulation at 25 and 35 °C. More N2O accumulation was observed during denitritation at the higher temperature as compared with full denitrification and low temperature tests. The highest nitrite concentration tested in this study (25 mg/L NO2−N and pH 8.0) did not show inhibitory effect on N2O reduction. It was found that the major cause of more N2O accumulation during denitrification at higher temperature was due to higher N2O production rate and lower N2O solubility. Specific nitrate, nitrite, and N2O reduction rates increased 62, 61, and 41 %, respectively, when temperature rose from 25 to 35 °C. The decrease of N2O solubility in mixed liquor at 35 °C (when compared to 25 °C) resulted in faster diffusing rate of N2O from liquid to gas phase. It was also more difficult for gas phase N2O to be re-dissolved. The diffused N2O was then accumulated in the headspace, which was not available for denitrification by denitrifiers. The results of this study suggest higher temperature may worsen N2O emission from wastewater treatment plants (WWTPs). Accepted version

Biomaterials recovery from wasted activated sludge has become an increasing interesting research topic. The purpose of this study was to systemically evaluate different sludge disintegration methods (ultrasonic, alkaline, and thermal treatments) for protein solubilisation from waste activated sludge (WAS). Compared to control without treatment, the soluble protein concentration increased by 11, 23 and 12 times under the optimal treatment conditions (ultrasonic treatment of 1 W mL−1, alkaline treatment of pH 12 and thermal treatment at 80 °C). The increased soluble protein were significantly correlated with the release of total organic carbon (TOC), total dissolved nitrogen (TDN) and total organic nitrogen (TON) in soluble EPS, and the degradation of above parameters in tightly bound EPS. For all sludge samples treated by various methods, tyrosine-like protein with molecular weight less than 20 kDa predominated, and alkaline treatment at pH 12 showed the highest protein dominance. Further surface analysis of sludge by X-ray photoelectron spectroscopy indicated this might be related with the significant protein-N conversion occurred at pH 12. The economic analysis indicated alkaline treatment at pH 12 was economically feasible with a net saving of 25.57 USD per ton wet sludge compared to conventional sludge treatment and disposal method. Accepted version

In this study, a mixture of primary and wasted activated sludge was fermented in a semi-continuous reactor aiming for enhanced volatile fatty acids (VFAs) production. The reactor was subjected to a stepwise pH increase from 7 to 10 during approximately 130 days of operation. The result revealed that the maximum acidification was obtained at pH 8.9 (21%) resulting in the maximum production of VFAs (423.22 ± 25.49 mg COD/g VSS), while the maximum hydrolysis efficiency was observed at pH 9.9 (42%). The high pH was effective in releasing dissolved organic matter (DOM) including protein, carbohydrate, building blocks and low molecular weight (LMW) neutrals. More LMW DOMs were released than high molecular weight (HMW) DOMs fractions at higher pH. pH 9.9 favored hydrolysis of HMW DOMs while it did not enhance the acidogenesis of LMW DOMs. The microbial community analysis showed that the relative abundance of phyla Actinobacteria and Proteobacteria increased with the increased pH, which may lead to the maximum hydrolysis at pH 9.9. At pH 8.9, class Clostridia (59.16%) was the most dominant population where the maximum acidification (21%) was obtained. This suggested that the dominance of Clostridia was highly related to acidification extent. The relative abundance of Euryarchaeota decreased significantly from 58% to 2% with increased pH. Accepted version

In this study, a thermophilic (65°C) single-stage wasted activated sludge (WAS) digester was established and the effects of solid retention time (SRT) on the reactor performance were investigated. The result showed that the optimum SRT was 6days with methane yield of 186.16mL/g VS. It was found that SRT had little effect on the hydrolysis and volatile solids (VS) destruction, and the high temperature employed seemed sufficient to achieve maximum hydrolysis and VS destruction performance. Longer SRT, however, promoted the release of recalcitrant compounds and impaired acidification, leading to the low methane yield. The microbial community analysis revealed that the dominant pathway for methane production was through syntrophic activity of acetate oxidizing bacteria and hydrogenotrophic methanogens while acetoclastic methanogens were absent in the system.